Published June 4, 2019
Open-Source code gets pushed every single day. In some cases, code is so useful to others, that the project gains a certain popularity. In this article, we handpicked a few of the most exciting, fast growing Python Libraries of this year.
Created: April 2019
Nowadays, most fully fledged IDEs offer a great debugger for Python. A debugger allows you to step through your code line-by-line, inspect certain variables and see where things go wrong. If you’re not working with an IDE, setting these up can be tedious though.
PySnooper is an alternative for that. The library is set up with two lines (the import and a decorator) and will record every call and line of code in the decorated function, and then dump it either to stdout or a file.
Apart from that PySnooper also allows you to watch certain variables with a single line code change and can even debug multithreaded programs.
The 2 month old project has been under constant development and we’re excited to see where it goes!
Created: February 2019
Leon is a Open-Source Personal Assistant, not unlike other helpers like Siri and Google Assistant, developed by the team behind https://getleon.ai/.
The app itself seems to technically be a Node.js app, but with large parts (34% at the time of writing) Python for the Natural Language Processing.
Currently, the in-development PA seems to be able to listen to a hotword (“Hey Siri”), to understand text and voice, able to understand common english expressions, is able to access certain modules to perform computations and check certain integrations, such as the Calendar, so-called “Checkers”, network statistics, trends and more.
Created: February 2019
Bullet is an incredibly helpful developer tool, being able to easily create custom CLI prompts, including simple [y/n] prompts, but also colorfully styled choice lists, free-text inputs, password prompts and more.
Bullet should be on the horizon of anyone building a developer tool (API CLI or similar), as it can make any setup process and/or CLI application more user-friendly.
Created: End of 2018 (real development started Jan 2019)
Ever wanted to get started with Machine Learning? Automl-gs might just be the easiest way to do so.
The library comes pre-shipped with some framework support. You only need to provide an annotated CSV and AutoML will automatically choose and compare some suitable models.
The library also comes with a ton of pre-shipped goodies: Code is automatically executed on a free TPU for the quickest possible training, messy datasets get filtered and parsed automatically, it generated native python code for integration in other places and finally, it automatically finetunes hyperparameters and compares models.
What else do you need?